RiboSphere: Learning Unified and Efficient Representations of RNA Structures

• URL: http://arxiv.org/abs/2603.19636v1
• Date: Fri, 20 Mar 2026 04:36:35 GMT
• Title: RiboSphere: Learning Unified and Efficient Representations of RNA Structures
• Abstract summary: RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce.<n>We introduce emphRiboSphere, a framework that learns emphdiscrete geometric representations of RNA by combining vector quantization with flow matching.
• Score: 57.40815107640066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce. We introduce \emph{RiboSphere}, a framework that learns \emph{discrete} geometric representations of RNA by combining vector quantization with flow matching. Our design is motivated by the modular organization of RNA architecture: complex folds are composed from recurring structural motifs. RiboSphere uses a geometric transformer encoder to produce SE(3)-invariant (rotation/translation-invariant) features, which are discretized with finite scalar quantization (FSQ) into a finite vocabulary of latent codes. Conditioned on these discrete codes, a flow-matching decoder reconstructs atomic coordinates, enabling high-fidelity structure generation. We find that the learned code indices are enriched for specific RNA motifs, suggesting that the model captures motif-level compositional structure rather than acting as a purely compressive bottleneck. Across benchmarks, RiboSphere achieves strong performance in structure reconstruction (RMSD 1.25\,Å, TM-score 0.84), and its pretrained discrete representations transfer effectively to inverse folding and RNA--ligand binding prediction, with robust generalization in data-scarce regimes.

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